A persistent bias present in many image datasets, ImageNet [1]}, is that the subjects of interest are generally photographed from a forward-facing, canonical, orientation. When, for example, taking a photograph of a television, few would choose to take this photograph crouched on the floor behind the television. This impact of this bias was studied by Alcorn  [2]} who find that modern computer vision systems are highly susceptible to deviations from canonical poses. This is more than a theoretical problem: computer vision systems deployed in the real world will frequently encounter objects in non-canonical orientations and in many applications, autonomous driving, it will be safety critical that they behave well.
